Mitigating the impact of errors in travel time reporting on mode choice modelling

Varotto, S.F.; Glerum, Aurélie; Stathopoulos, Amanda; Bierlaire, Michel; Longo, Giovanni Antonio

doi:10.1016/j.jtrangeo.2017.05.016

Cited by 37 publications

(16 citation statements)

References 36 publications

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“…However, the HCM accounting for measurement errors in time and cost variables generates significantly higher elasticities. This finding is consistent with the results in Varotto et al (2017).…”

Section: Discussionsupporting

confidence: 94%

“…Looking at the differences between the HCM and the MNL models, the HCM gives larger elasticities for all modes. This finding is consistent with the results in Varotto et al (2017), where they observe a 65% increase of the time elasticity value when using a HCM formulation. While this leads to values in our work for the public transport and car passenger cost elasticities that are outside the ranges indicated in the literature, these can reasonably be associated with the ways in which costs have been calculated and, in the case of car passengers, with the small sample size.…”

Section: Elasticitiessupporting

confidence: 92%

“…Latent variables are known only up to the distributions . In practice, most latent variables are assumed to have normal priors, see for instance Walker et al (2010), Díaz et al (2015, Vij and Walker (2016) or Varotto et al (2017), even in cases where the represented variable cannot take negative values. The use of normal distributions is supported by Bartholomew et al (2011) in their review of the general linear latent variable model; where the authors argued that the latent variable prior distribution can essentially be arbitrary, as the choice of seemed to have very little effect on the parameter estimates.…”

Section: -Latent Variable Distributionsmentioning

confidence: 99%

“…However, in a mode choice model as in this study, the estimated VoTs are representative for all travellers (not only the car drivers or PT users). Previous studies reporting VoT from HCMs accounting for measurement errors (Walker, 2010;Varotto, 2017) only treat the time as latent variables. They found higher time parameters, which translates into a higher VoT.…”

Section: Values Of Timementioning

confidence: 99%

“…The first application of the HCM to account for measurement errors can be found in Walker et al (2010), where a latent variable approach is introduced to deal with error-prone travel times. Using the same methodology, Guevara (2015) and Varotto et al (2017) investigate how the time parameter changes when accounting for measurement errors. Furthermore, Walker et al (2010) and Vij and Walker (2016) use Monte Carlo experiments to show that the estimated parameters converge to their true value as the model accounts for measurement errors in the input variables.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Quantifying errors in travel time and cost by latent variables

Varela

Börjesson

Daly

2018

Transportation Research Part B: Methodological

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Travel time and travel cost are key variables for explaining travel behaviour and deriving the value of time. However, a general problem in transport modelling is that these variables are subject to measurement errors in transport network models. In this paper we show how to assess the magnitude of the measurement errors in travel time and travel cost by latent variables, in a large-scale travel demand model. The case study for Stockholm commuters shows that assuming multiplicative measurement errors for travel time and cost result in a better fit than additive ones; however, when measurement errors are modelled, the estimated time and cost parameters are robust to the modelling assumptions. Moreover, our results suggest that measurement errors in our dataset are larger for the travel cost than for the travel time, and that measurement errors are larger in self-reported travel time than software-calculated travel time for car-driver and car-passenger, and of similar magnitude for public transport. Among self-reported travel times, car-passenger has the largest errors, followed by car-driver and public transport, and for the softwarecalculated times, public transport exhibits larger errors than car. These errors, if not corrected, lead to biases in measures derived from the models, such as elasticity and values of travel time.

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Section: Discussionsupporting

confidence: 94%

Section: Elasticitiessupporting

confidence: 92%

Section: -Latent Variable Distributionsmentioning

confidence: 99%

Section: Values Of Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantifying errors in travel time and cost by latent variables

Varela

Börjesson

Daly

2018

Transportation Research Part B: Methodological

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Ordinal Logistic Regression Modeling Research on Decreasing Perceived Metro Transfer Time

Feng

Hua

Niu

2020

Green, Smart and Connected Transportation Systems

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Affective experience in a virtual crowd regulates perceived travel time

et al. 2022

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Time sometimes feels like it is flying by or slowing down. Previous research indicates objective number of items, subjective affect, and heart rate all can influence the experience of time. While these factors are usually tested in isolation with simple stimuli in the laboratory, here we examined them together in the ecological context of a virtual subway ride. We hypothesized that subjective affective experience associated with objective crowding lengthens subjective trip duration. Participants (N = 41) experienced short (1-2 min) immersive virtual reality subway trips with different levels of public crowding. Consistent with the immersive nature of decreased interpersonal virtual space, increased crowding decreased pleasantness and increased the unpleasantness of a trip. Virtual crowding also lengthened perceived trip duration. The presence of one additional person per square meter of the train significantly increased perceived travel time by an average of 1.8 s. Degree of pleasant relative to unpleasant affect mediated why crowded trips felt longer. Independently of crowding and affect, heart rate changes were related to experienced trip time. These results demonstrate socioemotional regulation of the experience of time and that effects of social crowding on perception and affect can be reliably created during a solitary virtual experience. This study demonstrates a novel use of Virtual Reality technology for testing psychological theories in ecologically valid and highly controlled settings.

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Mitigating the impact of errors in travel time reporting on mode choice modelling

Cited by 37 publications

References 36 publications

Quantifying errors in travel time and cost by latent variables

Quantifying errors in travel time and cost by latent variables

Ordinal Logistic Regression Modeling Research on Decreasing Perceived Metro Transfer Time

Affective experience in a virtual crowd regulates perceived travel time

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